Search Results for author:
Found 65 papers, 34 papers with code
Perceiving Unseen 3D Objects by Poking the Objects
We present a novel approach to interactive 3D object perception for robots.
Learning Neural Volumetric Representations of Dynamic Humans in Minutes
In this paper, we propose a novel method for learning neural volumetric videos of dynamic humans from sparse view videos in minutes with competitive visual quality.
Painting 3D Nature in 2D: View Synthesis of Natural Scenes from a Single Semantic Mask
We introduce a novel approach that takes a single semantic mask as input to synthesize multi-view consistent color images of natural scenes, trained with a collection of single images from the Internet.
OnePose++: Keypoint-Free One-Shot Object Pose Estimation without CAD Models
We propose a new method for object pose estimation without CAD models.
Ponder: Point Cloud Pre-training via Neural Rendering
We propose a novel approach to self-supervised learning of point cloud representations by differentiable neural rendering.
Reconstructing Hand-Held Objects from Monocular Video
The key idea is that the hand motion naturally provides multiple views of the object and the motion can be reliably estimated by a hand pose tracker.
QuickPose: Real-time Multi-view Multi-person Pose Estimation in Crowded Scenes
The key challenge of this problem is to efficiently match 2D observations across multiple views.
Ranked #2 on
3D Multi-Person Pose Estimation
on Shelf
3D Multi-Person Pose Estimation
Multi-Person Pose Estimation
PlanarRecon: Real-time 3D Plane Detection and Reconstruction from Posed Monocular Videos
We present PlanarRecon -- a novel framework for globally coherent detection and reconstruction of 3D planes from a posed monocular video.
LADDER: Latent Boundary-guided Adversarial Training
To achieve a better trade-off between standard accuracy and adversarial robustness, we propose a novel adversarial training framework called LAtent bounDary-guided aDvErsarial tRaining (LADDER) that adversarially trains DNN models on latent boundary-guided adversarial examples.
Neural 3D Reconstruction in the Wild
We are witnessing an explosion of neural implicit representations in computer vision and graphics.
OnePose: One-Shot Object Pose Estimation without CAD Models
We propose a new method named OnePose for object pose estimation.
Ray Priors through Reprojection: Improving Neural Radiance Fields for Novel View Extrapolation
Neural Radiance Fields (NeRF) have emerged as a potent paradigm for representing scenes and synthesizing photo-realistic images.
Neural 3D Scene Reconstruction with the Manhattan-world Assumption
Based on the Manhattan-world assumption, planar constraints are employed to regularize the geometry in floor and wall regions predicted by a 2D semantic segmentation network.
Modeling Indirect Illumination for Inverse Rendering
The key insight is that indirect illumination can be conveniently derived from the neural radiance field learned from input images instead of being estimated jointly with direct illumination and materials.
Semantic keypoint-based pose estimation from single RGB frames
Empirically, we show that our approach can accurately recover the 6-DoF object pose for both instance- and class-based scenarios even against a cluttered background.
Panoptic NeRF: 3D-to-2D Label Transfer for Panoptic Urban Scene Segmentation
In this work, we present a novel 3D-to-2D label transfer method, Panoptic NeRF, which aims for obtaining per-pixel 2D semantic and instance labels from easy-to-obtain coarse 3D bounding primitives.
Learning Hierarchical Cross-Modal Association for Co-Speech Gesture Generation
To enhance the quality of synthesized gestures, we develop a contrastive learning strategy based on audio-text alignment for better audio representations.
Ranked #2 on
Gesture Generation
on TED Gesture Dataset
Animatable Implicit Neural Representations for Creating Realistic Avatars from Videos
Some recent works have proposed to decompose a non-rigidly deforming scene into a canonical neural radiance field and a set of deformation fields that map observation-space points to the canonical space, thereby enabling them to learn the dynamic scene from images.
Visual Sound Localization in the Wild by Cross-Modal Interference Erasing
Specifically, we observe that the previous practice of learning only a single audio representation is insufficient due to the additive nature of audio signals.
Efficient Neural Radiance Fields for Interactive Free-viewpoint Video
We propose a novel scene representation, called ENeRF, for the fast creation of interactive free-viewpoint videos.
Edge but not Least: Cross-View Graph Pooling
Graph neural networks have emerged as a powerful model for graph representation learning to undertake graph-level prediction tasks.
Neural Rays for Occlusion-aware Image-based Rendering
On such a 3D point, these generalization methods will include inconsistent image features from invisible views, which interfere with the radiance field construction.
VS-Net: Voting with Segmentation for Visual Localization
To address this problem, we propose a novel visual localization framework that establishes 2D-to-3D correspondences between the query image and the 3D map with a series of learnable scene-specific landmarks.
Animatable Neural Radiance Fields for Modeling Dynamic Human Bodies
Moreover, the learned blend weight fields can be combined with input skeletal motions to generate new deformation fields to animate the human model.
Reconstructing 3D Human Pose by Watching Humans in the Mirror
In this paper, we introduce the new task of reconstructing 3D human pose from a single image in which we can see the person and the person's image through a mirror.
LoFTR: Detector-Free Local Feature Matching with Transformers
We present a novel method for local image feature matching.
Ranked #1 on
Image Matching
on IMC PhotoTourism
(using extra training data)
NeuralRecon: Real-Time Coherent 3D Reconstruction from Monocular Video
We present a novel framework named NeuralRecon for real-time 3D scene reconstruction from a monocular video.
Generative Transition Mechanism to Image-to-Image Translation via Encoded Transformation
To benefit the generalization ability of the translation model, we propose transition encoding to facilitate explicit regularization of these two {kinds} of consistencies on unseen transitions.
Human-Understandable Decision Making for Visual Recognition
The widespread use of deep neural networks has achieved substantial success in many tasks.
You Don't Only Look Once: Constructing Spatial-Temporal Memory for Integrated 3D Object Detection and Tracking
In this work, we propose a novel system for integrated 3D object detection and tracking, which uses a dynamic object occupancy map and previous object states as spatial-temporal memory to assist object detection in future frames.
Neural Body: Implicit Neural Representations with Structured Latent Codes for Novel View Synthesis of Dynamic Humans
To this end, we propose Neural Body, a new human body representation which assumes that the learned neural representations at different frames share the same set of latent codes anchored to a deformable mesh, so that the observations across frames can be naturally integrated.
Learning Hybrid Representations for Automatic 3D Vessel Centerline Extraction
The main idea is to use CNNs to learn local appearances of vessels in image crops while using another point-cloud network to learn the global geometry of vessels in the entire image.
SMAP: Single-Shot Multi-Person Absolute 3D Pose Estimation
Recovering multi-person 3D poses with absolute scales from a single RGB image is a challenging problem due to the inherent depth and scale ambiguity from a single view.
3D Depth Estimation
3D Multi-Person Pose Estimation (absolute)
+3
Motion Capture from Internet Videos
Therefore, we propose to capture human motion by jointly analyzing these Internet videos instead of using single videos separately.
Coherent Reconstruction of Multiple Humans from a Single Image
Our goal is to train a single network that learns to avoid these problems and generate a coherent 3D reconstruction of all the humans in the scene.
Ranked #1 on
3D Human Reconstruction
on AGORA
Learning Feature Descriptors using Camera Pose Supervision
Recent research on learned visual descriptors has shown promising improvements in correspondence estimation, a key component of many 3D vision tasks.
Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation
In this paper, we propose a novel system named Disp R-CNN for 3D object detection from stereo images.
3D Object Detection From Stereo Images
Disparity Estimation
+2
Monocular Human Pose and Shape Reconstruction using Part Differentiable Rendering
In this paper, we introduce body part segmentation as critical supervision.
Ranked #66 on
3D Human Pose Estimation
on Human3.6M
(PA-MPJPE metric)
Deep Snake for Real-Time Instance Segmentation
Based on deep snake, we develop a two-stage pipeline for instance segmentation: initial contour proposal and contour deformation, which can handle errors in object localization.
Ranked #2 on
Semantic Contour Prediction
on Sbd val
GIFT: Learning Transformation-Invariant Dense Visual Descriptors via Group CNNs
Instead of feature pooling, we use group convolutions to exploit underlying structures of the extracted features on the group, resulting in descriptors that are both discriminative and provably invariant to the group of transformations.
Latent Adversarial Defence with Boundary-guided Generation
The proposed LAD method improves the robustness of a DNN model through adversarial training on generated adversarial examples.
Learning Transformation Synchronization
Our approach alternates between transformation synchronization using weighted relative transformations and predicting new weights of the input relative transformations using a neural network.
Fast and Robust Multi-Person 3D Pose Estimation from Multiple Views
This paper addresses the problem of 3D pose estimation for multiple people in a few calibrated camera views.
Ranked #11 on
3D Multi-Person Pose Estimation
on Campus
Path-Invariant Map Networks
Optimizing a network of maps among a collection of objects/domains (or map synchronization) is a central problem across computer vision and many other relevant fields.
Extreme Relative Pose Estimation for RGB-D Scans via Scene Completion
In particular, instead of only performing scene completion from each individual scan, our approach alternates between relative pose estimation and scene completion.
PVNet: Pixel-wise Voting Network for 6DoF Pose Estimation
We further create a Truncation LINEMOD dataset to validate the robustness of our approach against truncation.
Ranked #2 on
6D Pose Estimation using RGB
on YCB-Video
(Mean AUC metric)
Learning to Estimate 3D Human Pose and Shape from a Single Color Image
The proposed approach outperforms previous baselines on this task and offers an attractive solution for direct prediction of 3D shape from a single color image.
Ranked #88 on
3D Human Pose Estimation
on Human3.6M
(PA-MPJPE metric)
Ordinal Depth Supervision for 3D Human Pose Estimation
This information can be acquired by human annotators for a wide range of images and poses.
Ranked #1 on
Monocular 3D Human Pose Estimation
on Human3.6M
(Use Video Sequence metric)
Human Motion Capture Using a Drone
Current motion capture (MoCap) systems generally require markers and multiple calibrated cameras, which can be used only in constrained environments.
Multi-Image Semantic Matching by Mining Consistent Features
This work proposes a multi-image matching method to estimate semantic correspondences across multiple images.
Fast Multi-Image Matching via Density-Based Clustering
We consider the problem of finding consistent matches across multiple images.
Polar Transformer Networks
The result is a network invariant to translation and equivariant to both rotation and scale.
Harvesting Multiple Views for Marker-less 3D Human Pose Annotations
In this paper, we present a geometry-driven approach to automatically collect annotations for human pose prediction tasks.
Ranked #26 on
Weakly-supervised 3D Human Pose Estimation
on Human3.6M
6-DoF Object Pose from Semantic Keypoints
This paper presents a novel approach to estimating the continuous six degree of freedom (6-DoF) pose (3D translation and rotation) of an object from a single RGB image.
Ranked #1 on
Keypoint Detection
on Pascal3D+
MonoCap: Monocular Human Motion Capture using a CNN Coupled with a Geometric Prior
Recovering 3D full-body human pose is a challenging problem with many applications.
Coarse-to-Fine Volumetric Prediction for Single-Image 3D Human Pose
This paper addresses the challenge of 3D human pose estimation from a single color image.
Ranked #15 on
3D Human Pose Estimation
on HumanEva-I
Single Image Pop-Up From Discriminatively Learned Parts
We introduce a new approach for estimating a fine grained 3D shape and continuous pose of an object from a single image.
Sparseness Meets Deepness: 3D Human Pose Estimation from Monocular Video
Here, two cases are considered: (i) the image locations of the human joints are provided and (ii) the image locations of joints are unknown.
Ranked #31 on
Monocular 3D Human Pose Estimation
on Human3.6M
Sparse Representation for 3D Shape Estimation: A Convex Relaxation Approach
We investigate the problem of estimating the 3D shape of an object defined by a set of 3D landmarks, given their 2D correspondences in a single image.
Ranked #95 on
3D Human Pose Estimation
on Human3.6M
(PA-MPJPE metric)
Multi-Image Matching via Fast Alternating Minimization
In this paper we propose a global optimization-based approach to jointly matching a set of images.
Pose and Shape Estimation with Discriminatively Learned Parts
We introduce a new approach for estimating the 3D pose and the 3D shape of an object from a single image.
3D Shape Estimation from 2D Landmarks: A Convex Relaxation Approach
We investigate the problem of estimating the 3D shape of an object, given a set of 2D landmarks in a single image.
Low-Rank Modeling and Its Applications in Image Analysis
In this paper, we review the recent advance of low-rank modeling, the state-of-the-art algorithms, and related applications in image analysis.
Active Contours with Group Similarity
In this paper, we propose to use the group similarity of object shapes in multiple images as a prior to aid segmentation, which can be interpreted as an unsupervised approach of shape prior modeling.
Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank Representation
To automate the analysis, object detection without a separate training phase becomes a critical task.
